P-144 An artificial intelligence algorithm demonstrates optimal performance for evaluating embryo genetic status at 120 hours post-fertilization

Abstract

Abstract Study question What is the effect of time-point on performance of a non-invasive artificial intelligence (AI) algorithm for evaluating embryo genetic status? Summary answer While predictive ability was maintained across different time-points on day 5, optimal performance for ranking and selecting euploid embryos was observed at 120 hours post-fertilization. What is known already Studies have shown that it is possible to develop computer vision-based AI algorithms capable of predicting embryo ploidy status using single images of blastocyst-stage embryos. The genetic status of embryos is linked to morphokinetic development, with aneuploidy generally resulting in earlier arrest. Given the dynamic nature of embryo development, it might be expected that the time-point selected for analysis could influence AI performance. The key questions remaining to be answered are to what extent is AI analysis affected by expansion grade, and what does this mean for selection of a time-point for evaluation? Study design, size, duration 2,683 images of day 5 blastocyst-stage embryos with matched ploidy outcomes from pre-implantation genetic testing for aneuploidies (PGT-A) were provided by 10 IVF clinics in the USA, Australia, Malaysia, and India. A subset of 182 embryos had images provided at 110, 115, and 120 hour time points (GERI and EmbryoScope time lapse systems). Participants/materials, setting, methods Images were analysed by a previously developed AI algorithm which evaluates the likelihood of an embryo being euploid according to PGT-A (Diakiw et al, 2022. Hum Reprod, Jul 30;37(8):1746-1759). Evaluation was performed on embryos of each expansion grade, and at three time-points on day 5. Correlations were assessed using Chi-squared test for trend, with pair-wise comparisons conducted using Student’s t-test. Performance was evaluated using ROC-AUC, and a simulated cohort ranking analysis method. Main results and the role of chance AI scores positively correlated with expansion grade, and expansion grade likewise correlated with an increasing proportion of euploid embryos. AI scores also increased over time on day 5, consistent with continued embryo expansion. Scores for grade 4 (expanded) embryos increased more than for grade 5 (hatching) embryos (+2.2-fold and +0.8-fold, respectively), indicative of continued expansion becoming limited at later stages. Despite the valid association of AI scores with expansion grade, results showed the AI could predict euploidy even amongst embryos of the same expansion grade (ROC-AUC ranging from 0.61−0.69). While predictive ability was maintained at each time-point on day 5, ROC-AUC values were highest at 120 hours (0.64, 0.64, and 0.68 for 110, 115, and 120 hours, respectively). Simulated cohort ranking analyses also showed that the AI performed best at 120 hours, selecting a euploid embryo as the top-ranked embryo in 77.1% of patient cohorts (71.4%, 75.1%, and 77.1% for 110, 115, and 120 hours, respectively). These results suggest that regardless of expansion grade, all embryos should be assessed at the same time-point on day 5, preferably closer to the 120 hour time-point. Limitations, reasons for caution The time-point analyses were conducted on a relatively small dataset, and therefore findings should be validated on a larger dataset including embryos of other expansion grades. The analysis was limited to day 5 post-fertilization, however it would be interesting to extend the study to embryos on day 6 also. Wider implications of the findings These results suggest that the AI is providing additional information regarding embryo genetic status, over and above that provided by known morphological parameters. The dynamic nature of AI score related to expansion is of interest as it relates to the optimal time-point for conducting analyses for selection of euploid embryos. Trial registration number Not applicable